One method to predict porosity in tight sandstone reservoirs based on SAO-LightBGM algorithm
Porosity is a decisive parameter in the evaluation on reservoirs'physical properties.Only a little data has been acquired from drilling and coring in NC area,central Sichuan Basin.In particular,it was a direct challenge to receive this parameter.And traditional methods for porosity prediction based on conventional logging data give rise to a large error or poor accuracy.So,taken the reservoirs of Xujiahe 4 Member as examples,an improved machine-learning algorithm,name-ly SAO-LightGBM,was put forward to clarify the physical properties of tight sandstone reservoirs in this area.Then,the al-gorithm was utilized to analyze the latent relationship of porosity to logging parameters,indicating its strong correlation with acoustic time difference,density,neutron porosity,resistivity and natural gamma.Finally,a prediction model was built on account of these parameters.Results show that(i)owing to the exclusive dual swarm mechanism together with both effi-cient enquiry and utilization strategy,SAO algorithm can quickly search an optimal hyper-parameter combination of Light-GBM,so as to scale up this model's prediction ability;and(ii)both mean absolute error and determination coefficient of SAO-LightGBM are 3.37%and 0.92,respectively.In conclusion,boasting more reliable ability in comparison with other regular models,SAO-LightGBM can predict the porosity very well,which plays a guiding role in reservoir research and lat-er exploitation in NC area.
Tight sandstonePorositySnow ablation optimization(SAO)algorithmLight gradient boosting machine(Light-GBM)machine learning algorithmprediction model
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